#### Title

Quantifying minimal noncollinearity among random points

#### Document Type

Article

#### Publication Date

1-1-2018

#### Abstract

© 2018 Society for Industrial and Applied Mathematics and by SIAM. Let φn,K denote the largest angle in all the triangles with vertices among the n points selected at random in a compact convex subset K of ℝd with nonempty interior, where d ≥ 2. It is shown that the distribution of the random variable (formula presented),where λd (K) is a certain positive real number which depends only on the dimension d and the shape of K, converges to the standard exponential distribution as n → ∞. By using the Steiner symmetrization, it is also shown that λd(K), which is referred to in the paper as the elongation of K, attains its minimum if and only if K is a ball B(d) in Rd. Finally, the asymptotics of λd (B(d)) for large d is determined.

#### Publication Title

Theory of Probability and its Applications

#### Recommended Citation

Pinelis, I.
(2018).
Quantifying minimal noncollinearity among random points.
*
Theory of Probability and its Applications,
62*(4), 604-616.
http://doi.org/10.1137/S0040585X97T988836

Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/12342